challenge_2
railroads
faostat
hotel_bookings
Author

Lai Wei

Published

October 26, 2022

Code 
library(tidyverse)
library(summarytools)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Challenge Overview

Today’s challenge is to

  1. read in a data set, and describe the data using both words and any supporting information (e.g., tables, etc)
  2. provide summary statistics for different interesting groups within the data, and interpret those statistics

Read in the Data

  • railroad*.csv or StateCounty2012.xls ⭐
  • FAOstat*.csv or birds.csv ⭐⭐⭐
  • hotel_bookings.csv ⭐⭐⭐⭐
Code 
hotel <- read.csv("_data/hotel_bookings.csv")

Read the hotel_bookings data as hotel.

Describe the data

Code 
hotel %>% 
  select(reservation_status) %>% 
  distinct()
  reservation_status
1          Check-Out
2           Canceled
3            No-Show

There are 3 different reservation status of the hotel, ‘check-out’, ‘canceled’ and ‘no-show’.

Provide Grouped Summary Statistics

Data summary

Code 
hotel %>% 
  summarise(across(c(country,market_segment),n_distinct))
  country market_segment
1     178              8

Count the number of unique 178 countries and 8 different market_segments of hotel data

Group_by() application

Code 
hotel %>% 
  group_by(reservation_status) %>% 
  summarise(num_adult = sum(adults,na.rm = TRUE),
            num_child = sum(children,na.rm = TRUE),
            num_baby = sum(babies, na.rm = TRUE))
# A tibble: 3 × 4
  reservation_status num_adult num_child num_baby
  <chr>                  <int>     <int>    <int>
1 Canceled               82095      4567      161
2 Check-Out             137534      7693      780
3 No-Show                 2007       143        8

In total, we can see how many adults, children and babies were checking in successfully. The information may help the hotel to arrange more facilities for children or babies.

Data summary visualization

Code 
print(summarytools::dfSummary(hotel,
                        varnumbers = FALSE,
                        plain.ascii  = FALSE, 
                        style        = "grid", 
                        graph.magnif = 0.70, 
                        valid.col    = FALSE),
      method = 'render',
      table.classes = 'table-condensed')

Data Frame Summary

hotel

Dimensions: 119390 x 32
Duplicates: 31994
Variable Stats / Values Freqs (% of Valid) Graph Missing
hotel [character]
1. City Hotel
2. Resort Hotel
79330(66.4%)
40060(33.6%)
 0 (0.0%)
is_canceled [integer]
Min : 0
Mean : 0.4
Max : 1
0:75166(63.0%)
1:44224(37.0%)
 0 (0.0%)
lead_time [integer]
Mean (sd) : 104 (106.9)
min ≤ med ≤ max:
0 ≤ 69 ≤ 737
IQR (CV) : 142 (1)
 479 distinct values 0 (0.0%)
arrival_date_year [integer]
Mean (sd) : 2016.2 (0.7)
min ≤ med ≤ max:
2015 ≤ 2016 ≤ 2017
IQR (CV) : 1 (0)
2015:21996(18.4%)
2016:56707(47.5%)
2017:40687(34.1%)
 0 (0.0%)
arrival_date_month [character]
1. August
2. July
3. May
4. October
5. April
6. June
7. September
8. March
9. February
10. November
[ 2 others ]
13877(11.6%)
12661(10.6%)
11791(9.9%)
11160(9.3%)
11089(9.3%)
10939(9.2%)
10508(8.8%)
9794(8.2%)
8068(6.8%)
6794(5.7%)
12709(10.6%)
 0 (0.0%)
arrival_date_week_number [integer]
Mean (sd) : 27.2 (13.6)
min ≤ med ≤ max:
1 ≤ 28 ≤ 53
IQR (CV) : 22 (0.5)
 53 distinct values 0 (0.0%)
arrival_date_day_of_month [integer]
Mean (sd) : 15.8 (8.8)
min ≤ med ≤ max:
1 ≤ 16 ≤ 31
IQR (CV) : 15 (0.6)
 31 distinct values 0 (0.0%)
stays_in_weekend_nights [integer]
Mean (sd) : 0.9 (1)
min ≤ med ≤ max:
0 ≤ 1 ≤ 19
IQR (CV) : 2 (1.1)
 17 distinct values 0 (0.0%)
stays_in_week_nights [integer]
Mean (sd) : 2.5 (1.9)
min ≤ med ≤ max:
0 ≤ 2 ≤ 50
IQR (CV) : 2 (0.8)
 35 distinct values 0 (0.0%)
adults [integer]
Mean (sd) : 1.9 (0.6)
min ≤ med ≤ max:
0 ≤ 2 ≤ 55
IQR (CV) : 0 (0.3)
 14 distinct values 0 (0.0%)
children [integer]
Mean (sd) : 0.1 (0.4)
min ≤ med ≤ max:
0 ≤ 0 ≤ 10
IQR (CV) : 0 (3.8)
0:110796(92.8%)
1:4861(4.1%)
2:3652(3.1%)
3:76(0.1%)
10:1(0.0%)
 4 (0.0%)
babies [integer]
Mean (sd) : 0 (0.1)
min ≤ med ≤ max:
0 ≤ 0 ≤ 10
IQR (CV) : 0 (12.3)
0:118473(99.2%)
1:900(0.8%)
2:15(0.0%)
9:1(0.0%)
10:1(0.0%)
 0 (0.0%)
meal [character]
1. BB
2. FB
3. HB
4. SC
5. Undefined
92310(77.3%)
798(0.7%)
14463(12.1%)
10650(8.9%)
1169(1.0%)
 0 (0.0%)
country [character]
1. PRT
2. GBR
3. FRA
4. ESP
5. DEU
6. ITA
7. IRL
8. BEL
9. BRA
10. NLD
[ 168 others ]
48590(40.7%)
12129(10.2%)
10415(8.7%)
8568(7.2%)
7287(6.1%)
3766(3.2%)
3375(2.8%)
2342(2.0%)
2224(1.9%)
2104(1.8%)
18590(15.6%)
 0 (0.0%)
market_segment [character]
1. Aviation
2. Complementary
3. Corporate
4. Direct
5. Groups
6. Offline TA/TO
7. Online TA
8. Undefined
237(0.2%)
743(0.6%)
5295(4.4%)
12606(10.6%)
19811(16.6%)
24219(20.3%)
56477(47.3%)
2(0.0%)
 0 (0.0%)
distribution_channel [character]
1. Corporate
2. Direct
3. GDS
4. TA/TO
5. Undefined
6677(5.6%)
14645(12.3%)
193(0.2%)
97870(82.0%)
5(0.0%)
 0 (0.0%)
is_repeated_guest [integer]
Min : 0
Mean : 0
Max : 1
0:115580(96.8%)
1:3810(3.2%)
 0 (0.0%)
previous_cancellations [integer]
Mean (sd) : 0.1 (0.8)
min ≤ med ≤ max:
0 ≤ 0 ≤ 26
IQR (CV) : 0 (9.7)
 15 distinct values 0 (0.0%)
previous_bookings_not_canceled [integer]
Mean (sd) : 0.1 (1.5)
min ≤ med ≤ max:
0 ≤ 0 ≤ 72
IQR (CV) : 0 (10.9)
 73 distinct values 0 (0.0%)
reserved_room_type [character]
1. A
2. B
3. C
4. D
5. E
6. F
7. G
8. H
9. L
10. P
85994(72.0%)
1118(0.9%)
932(0.8%)
19201(16.1%)
6535(5.5%)
2897(2.4%)
2094(1.8%)
601(0.5%)
6(0.0%)
12(0.0%)
 0 (0.0%)
assigned_room_type [character]
1. A
2. D
3. E
4. F
5. G
6. C
7. B
8. H
9. I
10. K
[ 2 others ]
74053(62.0%)
25322(21.2%)
7806(6.5%)
3751(3.1%)
2553(2.1%)
2375(2.0%)
2163(1.8%)
712(0.6%)
363(0.3%)
279(0.2%)
13(0.0%)
 0 (0.0%)
booking_changes [integer]
Mean (sd) : 0.2 (0.7)
min ≤ med ≤ max:
0 ≤ 0 ≤ 21
IQR (CV) : 0 (2.9)
 21 distinct values 0 (0.0%)
deposit_type [character]
1. No Deposit
2. Non Refund
3. Refundable
104641(87.6%)
14587(12.2%)
162(0.1%)
 0 (0.0%)
agent [character]
1. 9
2. NULL
3. 240
4. 1
5. 14
6. 7
7. 6
8. 250
9. 241
10. 28
[ 324 others ]
31961(26.8%)
16340(13.7%)
13922(11.7%)
7191(6.0%)
3640(3.0%)
3539(3.0%)
3290(2.8%)
2870(2.4%)
1721(1.4%)
1666(1.4%)
33250(27.8%)
 0 (0.0%)
company [character]
1. NULL
2. 40
3. 223
4. 67
5. 45
6. 153
7. 174
8. 219
9. 281
10. 154
[ 343 others ]
112593(94.3%)
927(0.8%)
784(0.7%)
267(0.2%)
250(0.2%)
215(0.2%)
149(0.1%)
141(0.1%)
138(0.1%)
133(0.1%)
3793(3.2%)
 0 (0.0%)
days_in_waiting_list [integer]
Mean (sd) : 2.3 (17.6)
min ≤ med ≤ max:
0 ≤ 0 ≤ 391
IQR (CV) : 0 (7.6)
 128 distinct values 0 (0.0%)
customer_type [character]
1. Contract
2. Group
3. Transient
4. Transient-Party
4076(3.4%)
577(0.5%)
89613(75.1%)
25124(21.0%)
 0 (0.0%)
adr [numeric]
Mean (sd) : 101.8 (50.5)
min ≤ med ≤ max:
-6.4 ≤ 94.6 ≤ 5400
IQR (CV) : 56.7 (0.5)
 8879 distinct values 0 (0.0%)
required_car_parking_spaces [integer]
Mean (sd) : 0.1 (0.2)
min ≤ med ≤ max:
0 ≤ 0 ≤ 8
IQR (CV) : 0 (3.9)
0:111974(93.8%)
1:7383(6.2%)
2:28(0.0%)
3:3(0.0%)
8:2(0.0%)
 0 (0.0%)
total_of_special_requests [integer]
Mean (sd) : 0.6 (0.8)
min ≤ med ≤ max:
0 ≤ 0 ≤ 5
IQR (CV) : 1 (1.4)
0:70318(58.9%)
1:33226(27.8%)
2:12969(10.9%)
3:2497(2.1%)
4:340(0.3%)
5:40(0.0%)
 0 (0.0%)
reservation_status [character]
1. Canceled
2. Check-Out
3. No-Show
43017(36.0%)
75166(63.0%)
1207(1.0%)
 0 (0.0%)
reservation_status_date [character]
1. 2015-10-21
2. 2015-07-06
3. 2016-11-25
4. 2015-01-01
5. 2016-01-18
6. 2015-07-02
7. 2016-12-07
8. 2015-12-18
9. 2016-02-09
10. 2016-04-04
[ 916 others ]
1461(1.2%)
805(0.7%)
790(0.7%)
763(0.6%)
625(0.5%)
469(0.4%)
450(0.4%)
423(0.4%)
412(0.3%)
382(0.3%)
112810(94.5%)
 0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.2.1)
2022-12-20

Explain and Interpret

I choose this data is because I did the middle level in summer session and this time I want to practice something which is more complex and fun for increasing my R skill.